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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
This is a fine-tuned version of Multilingual Bart trained in Russian for Grammatical Error Correction.
To initialize the model:
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
model = MBartForConditionalGeneration.from_pretrained("MRNH/mbart-russian-grammar-corrector")
To use the tokenizer:
tokenizer = MBart50TokenizerFast.from_pretrained("MRNH/mbart-russian-grammar-corrector", src_lang="ru_RU", tgt_lang="ru_RU")
input = tokenizer("I was here yesterday to studying",text_target="I was here yesterday to study", return_tensors='pt')
To generate text using the model:
output = model.generate(input["input_ids"],attention_mask=input["attention_mask"],forced_bos_token_id=tokenizer_it.lang_code_to_id["ru_RU"])
Training of the model is performed using the following loss computation based on the hidden state output h:
h.logits, h.loss = model(input_ids=input["input_ids"],
attention_mask=input["attention_mask"],
labels=input["labels"])
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